Defense Against Poisoning Attack via Evaluating Training Samples Using Multiple Spectral Clustering Aggregation Method

Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with deep learning requiring huge amounts of labelled samples. When compared with the deep learning method, humans can recognize a new image because our human visual system is naturally capable of extracting features from any object and comparing them. Inspired by this visual mechanism, we introduced the mechanism of comparison into deep learning method to realize better visual reconstruction by making full use of each sample and the relationship of the sample pair by learning to compare. In this way, we proposed a Siamese reconstruction network (SRN) method. By using the SRN, we improved upon the satisfying results on two fMRI recording datasets, providing 72.5% accuracy on the digit dataset and 44.6% accuracy on the character dataset. Essentially, this manner can increase the training data about from n samples to 2n sample pairs, which takes full advantage of the limited quantity of training samples. The SRN learns to converge sample pairs of the same class or disperse sample pairs of different class in feature space.

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A Method for Multi-HVDC Grouping Based on Spectral Clustering

2020 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia) ◽

10.1109/icpsasia48933.2020.9208360 ◽

2020 ◽

Author(s):

Xiaohui Wang ◽

Yu Bai ◽

Yadong Gao ◽

Dong Liu ◽

Yan Zhang ◽

...

Keyword(s):

Spectral Clustering

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Alignment and integration of complex networks by hypergraph-based spectral clustering

Physical Review E ◽

10.1103/physreve.86.056111 ◽

2012 ◽

Vol 86 (5) ◽

Cited By ~ 28

Author(s):

Tom Michoel ◽

Bruno Nachtergaele

Keyword(s):

Complex Networks ◽

Spectral Clustering

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A Novel Unsupervised Classification Method for Sandy Land Using Fully Polarimetric SAR Data

Remote Sensing ◽

10.3390/rs13030355 ◽

2021 ◽

Vol 13 (3) ◽

pp. 355

Author(s):

Weixian Tan ◽

Borong Sun ◽

Chenyu Xiao ◽

Pingping Huang ◽

Wei Xu ◽

...

Keyword(s):

Spectral Clustering ◽

Large Scale ◽

Clustering Algorithm ◽

Feature Vector ◽

Unsupervised Classification ◽

Classification Method ◽

Sandy Land ◽

Classification Methods ◽

The Many ◽

Representative Points

Classification based on polarimetric synthetic aperture radar (PolSAR) images is an emerging technology, and recent years have seen the introduction of various classification methods that have been proven to be effective to identify typical features of many terrain types. Among the many regions of the study, the Hunshandake Sandy Land in Inner Mongolia, China stands out for its vast area of sandy land, variety of ground objects, and intricate structure, with more irregular characteristics than conventional land cover. Accounting for the particular surface features of the Hunshandake Sandy Land, an unsupervised classification method based on new decomposition and large-scale spectral clustering with superpixels (ND-LSC) is proposed in this study. Firstly, the polarization scattering parameters are extracted through a new decomposition, rather than other decomposition approaches, which gives rise to more accurate feature vector estimate. Secondly, a large-scale spectral clustering is applied as appropriate to meet the massive land and complex terrain. More specifically, this involves a beginning sub-step of superpixels generation via the Adaptive Simple Linear Iterative Clustering (ASLIC) algorithm when the feature vector combined with the spatial coordinate information are employed as input, and subsequently a sub-step of representative points selection as well as bipartite graph formation, followed by the spectral clustering algorithm to complete the classification task. Finally, testing and analysis are conducted on the RADARSAT-2 fully PolSAR dataset acquired over the Hunshandake Sandy Land in 2016. Both qualitative and quantitative experiments compared with several classification methods are conducted to show that proposed method can significantly improve performance on classification.

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